Complex surveys a guide to analysis using R
A complete guide to carrying out complex survey analysis using R As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to...
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| Format: | eBook Book |
| Language: | English |
| Published: |
Hoboken, N.J
Wiley
2010
John Wiley & Sons, Incorporated Wiley-Blackwell |
| Edition: | 1st ed. |
| Series: | Wiley Series in Survey Methodology |
| Subjects: | |
| ISBN: | 9780470284308, 0470580054, 9780470580059, 0470284307, 9780470580066, 0470580062 |
| Online Access: | Get full text |
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Table of Contents:
- Intro -- Complex Surveys: A Guide to Analysis Using R -- Contents -- Acknowledgments -- Preface -- Acronyms -- 1 Basic Tools -- 1.1 Goals of inference -- 1.1.1 Population or process? -- 1.1.2 Probability samples -- 1.1.3 Sampling weights -- 1.1.4 Design effects -- 1.2 An introduction to the data -- 1.2.1 Real surveys -- 1.2.2 Populations -- 1.3 Obtaining the software -- 1.3.1 Obtaining R -- 1.3.2 Obtaining the survey package -- 1.4 Using R -- 1.4.1 Reading plain text data -- 1.4.2 Reading data from other packages -- 1.4.3 Simple computations -- Exercises -- 2. Simple and Stratified sampling -- 2.1 Analyzing simple random samples -- 2.1.1 Confidence intervals -- 2.1.2 Describing the sample to R -- 2.2 Stratified sampling -- 2.3 Replicate weights -- 2.3.1 Specifying replicate weights to R -- 2.3.2 Creating replicate weights in R -- 2.4 Other population summaries -- 2.4.1 Quantiles -- 2.4.2 Contingency tables -- 2.5 Estimates in subpopulations -- 2.6 Design of stratified samples -- Exercises -- 3. Cluster sampling -- 3.1 Introduction -- 3.1.1 Why clusters: the NHANES II design -- 3.1.2 Single-stage and multistage designs -- 3.2 Describing multistage designs to R -- 3.2.1 Strata with only one PSU -- 3.2.2 How good is the single-stage approximation? -- 3.2.3 Replicate weights for multistage samples -- 3.3 Sampling by size -- 3.3.1 Loss of information from sampling clusters -- 3.4 Repeated measurements -- Exercises -- 4. Graphics -- 4.1 Why is survey data different? -- 4.2 Plotting a table -- 4.3 One continuous variable -- 4.3.1 Graphs based on the distribution function -- 4.3.2 Graphs based on the density -- 4.4 Tho continuous variables -- 4.4.1 Scatterplots -- 4.4.2 Aggregation and smoothing -- 4.4.3 Scatterplot smoothers -- 4.5 Conditioning plots -- 4.6 Maps -- 4.6.1 Design and estimation issues -- 4.6.2 Drawing maps in R -- Exercises
- 5 Ratios and linear regression -- 5.1 Ratio estimation -- 5.1.1 Estimating ratios -- 5.1.2 Ratios for subpopulation estimates -- 5.1.3 Ratio estimators of totals -- 5.2 Linear regression -- 5.2.1 The least-squares slope as an estimated population summary -- 5.2.2 Regression estimation of population totals -- 5.2.3 Confounding and other criteria for model choice -- 5.2.4 Linear models in the survey package -- 5.3 Is weighting needed in regression models? -- Exercises -- 6 Categorical data regression -- 6.1 Logistic regression -- 6.1.1 Relative risk regression -- 6.2 Ordinal regression -- 6.2.1 Other cumulative link models -- 6.3 Loglinear models -- 6.3.1 Choosing models. -- 6.3.2 Linear association models -- Exercises -- 7 Post-stratification, raking and calibration -- 7.1 Introduction -- 7.2 Post-stratification -- 7.3 Raking -- 7.4 Generalized raking, GREG estimation, and calibration -- 7.4.1 Calibration in R -- 7.5 Basu's elephants -- 7.6 Selecting auxiliary variables for non-response -- 7.6.1 Direct standardization -- 7.6.2 Standard error estimation -- Exercises -- 8 Two-phase sampling -- 8.1 Multistage and multiphase sampling -- 8.2 Sampling for stratification -- 8.3 The case-control design -- 8.3.1 * Simulations: efficiency of the design-based estimator -- 8.3.2 Frequency matching -- 8.4 Sampling from existing cohorts -- 8.4.1 Logistic regression -- 8.4.2 Two-phase case-control designs in R -- 8.4.3 Survival analysis -- 8.4.4 Case-cohort designs in R -- 8.5 Using auxiliary information from phase one -- 8.5.1 Population calibration for regression models -- 8.5.2 Two-phase designs -- 8.5.3 Some history of the two-phase calibration estimator -- Exercises -- 9 Missing data -- 9.1 Item non-response -- 9.2 Two-phase estimation for missing data -- 9.2.1 Calibration for item non-response -- 9.2.2 Models for response probability
- 9.2.3 Effect on precision -- 9.2.4 * Doubly-robust estimators -- 9.3 Imputation of missing data -- 9.3.1 Describing multiple imputations to R -- 9.3.2 Example: NHANES III imputations -- Exercises -- 10 * Causal inference -- 10.1 IPTW estimators -- 10.1.1 Randomized trials and calibration -- 10.1.2 Estimated weights for IPTW -- 10.1.3 Double robustness -- 10.2 Marginal Structural Models -- Appendix A: Analytic Details -- A.1 Asymptotics -- A.1.1 Embedding in an infinite sequence -- A.1.2 Asymptotic unbiasedness -- A.1.3 Asymptotic normality and consistency -- A.2 Variances by linearization -- A.2.1 Subpopulation inference -- A.3 Tests in contingency tables -- A.4 Multiple imputation -- A.5 Calibration and influence functions -- A.6 Calibration in randomized trials and ANCOVA -- Appendix B: Basic R -- B.1 Reading data -- B.1.1 Plain text data -- B.2 Data manipulation -- B.2.1 Merging -- B.2.2 Factors -- B.3 Randomness -- B.4 Methods and objects -- B.5 * Writing functions -- B.5.1 Repetition -- B.5.2 Strings -- Appendix C: Computational details -- C.1 Linearization -- C.1.1 Generalized linear models and expected information -- C.2 Replicate weights -- C.2.1 Choice of estimators -- C.2.2 Hadamard matrices -- C.3 Scatterplot smoothers -- C.4 Quantiles -- C.5 Bug reports and feature requests -- Appendix D: Database-backed design objects -- D.1 Large data -- D.2 Setting up database interfaces -- D.2.1 ODBC -- D.2.2 DBI -- Appendix E: Extending the package -- E.1 A case study: negative binomial regression -- E.2 Using a Poisson model -- E.3 Replicate weights -- E.4 Linearization -- References -- Author Index -- Topic Index